Baselines:
For the baseline models we use the Adam optimizer as well. We experiment with both random initialization as well as kaiming initialization for the resnet based baseline methods [1]}.
We do a grid search to find the best learning rate between \([1e-3, 1e-4]\)  and report numbers with it.
Also, given the small train data size we test with multiple batch sizes of 4, 8 and 16 and report numbers with the best results.
